Objective: 1) identify all closely genetic pairs < x core variants 2) filter pairs that have mortality metadata (survived <-> died or survived <-> survived or died <-> died) 3) Extract all mutations/genes corresponding for this different “mortality switches/non-switches” 4) Identify all the mutations specific to survived <-> died switches (<=> mut_survived<->died - mut_survived<->survived (- mut_died<->died ?)) 5) From all closely related paired isolates filter the one that have been already phenotyped 6) Calculate changes (delta) for each measured phenotype (all GC and PI parameters) 7) Investigate difference in delta of all parameter changes for survived <-> died VS survived <-> survived (VS died<->died -> Is there a systematic significant change between theses parameters (absolute delta)? -> Are these changes directionnal (eg decrease in growth rate or AUC_cell_death) 8) Accessory question are there phenotypic changes not associated with detected genotypic changes -> should we investigate structural variants (nanopore pacbio ?)
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = here::here())
msg <- stringr::str_c("My directory is ", here::here())
message(msg)
## My directory is /Users/giulieris/OneDrive - The University of Melbourne/R/VANANZ_phenotypes_github
library(tidyverse)
library(magrittr)
rm(list = ls())
source("Functions/all_functions.R")
snp.dist.mat <- read.csv("Data_analysis/Genetic_pairs_analysis/VANANZ_core.aln.dist.mat", sep = "\t") %>%
as.matrix(.)
str(snp.dist.mat)
## chr [1:844, 1:845] "BPH2700" "BPH2701" "BPH2702" "BPH2703" "BPH2704" ...
## - attr(*, "dimnames")=List of 2
## ..$ : NULL
## ..$ : chr [1:845] "snp.dists.0.6.3" "BPH2700" "BPH2701" "BPH2702" ...
# put 1st column as row name and remove 1st column
row.names(snp.dist.mat) <- snp.dist.mat[,1]
snp.dist.mat <- snp.dist.mat[,2:845]
str(snp.dist.mat)
## chr [1:844, 1:844] " 0" "18224" " 9375" " 9267" "25534" "25539" "29327" ...
## - attr(*, "dimnames")=List of 2
## ..$ : chr [1:844] "BPH2700" "BPH2701" "BPH2702" "BPH2703" ...
## ..$ : chr [1:844] "BPH2700" "BPH2701" "BPH2702" "BPH2703" ...
require(harrietr)
## Loading required package: harrietr
## Registered S3 method overwritten by 'treeio':
## method from
## root.phylo ape
snp.dist.df <- harrietr::melt_dist(snp.dist.mat) %>%
mutate(dist = as.integer(dist)) %>%
filter(iso1 != "Reference") # remove reference
head(snp.dist.df)
## iso1 iso2 dist
## 1 BPH2701 BPH2700 18224
## 2 BPH2702 BPH2700 9375
## 3 BPH2703 BPH2700 9267
## 4 BPH2704 BPH2700 25534
## 5 BPH2705 BPH2700 25539
## 6 BPH2706 BPH2700 29327
hist(snp.dist.df$dist, breaks = 1000)
hist(snp.dist.df$dist, breaks = 100000, xlim = c(0,100))
close_strain_symetrical.df <- close_strain.df %>%
mutate(isoA = iso1, isoB = iso2) %>%
mutate(iso1 = isoB, iso2 = isoA) %>%
select(iso1, iso2, dist) %>%
rbind(close_strain.df, .)
str(close_strain_symetrical.df)
## 'data.frame': 2484 obs. of 3 variables:
## $ iso1: chr "BPH2705" "BPH2709" "BPH2780" "BPH2896" ...
## $ iso2: chr "BPH2704" "BPH2708" "BPH2710" "BPH2710" ...
## $ dist: int 21 20 18 20 26 20 9 16 16 29 ...
# close_strain_symetrical.df %>%
# arrange(iso1) %>%
# mutate(pair_id = str_c("GP-", formatC(row_number(), width = 4, format = "d", flag = "0"))) %>%
# select(pair_id, iso1, iso2) %>%
# write_tsv("Ideas_Grant_2020_analysis/Genetic_pairs_table/genetic_pairs.tab", col_names = F)
ST_to_CC.df <- read.csv("Ideas_Grant_2020_analysis/Raw_data/Saureus_CC_to_ST.csv")
# !!! use corrected mortality metadata, the following changes in the sample metadata table have been made:
# 1) all isolates recovered from patient who survived are now labeled "survived" (previously only first isolate)
# 2) only the last isolate from patient who died are labeled "died" (previously on l first)
sample_meta_iso1.df <- read.csv("Ideas_Grant_2020_analysis/Raw_data/strain_metadata_corrected_mortality_with_controls.csv") %>%
merge(., ST_to_CC.df, by = "ST", all.x = T) %>%
select_all(.funs = funs(paste0("iso1_", .)))
## Warning: `funs()` is deprecated as of dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
sample_meta_iso2.df <- read.csv("Ideas_Grant_2020_analysis/Raw_data/strain_metadata_corrected_mortality_with_controls.csv") %>%
merge(., ST_to_CC.df, by = "ST", all.x = T) %>%
select_all(.funs = funs(paste0("iso2_", .)))
close_strain_symetrical.df <- merge(close_strain_symetrical.df,
sample_meta_iso1.df,
by.x = "iso1",
by.y = "iso1_sample_id") %>%
merge(.,
sample_meta_iso2.df,
by.x = "iso2",
by.y = "iso2_sample_id") %>%
mutate(CC = ifelse(is.na(iso1_CC), yes = as.character(iso2_CC), no = as.character(iso1_CC)))
sample_GC_param.df <- read.csv("Data_analysis/processed_data/processed_median_parameters_GC.csv") %>%
filter(strain_group != "CONTROL") %>%
select(sample_id, ends_with("OD"))
sample_PI_param.df <- read.csv("Data_analysis/processed_data/processed_median_parameters_PI.csv") %>%
filter(strain_group != "CONTROL") %>%
select(sample_id, ends_with("death"))
# merge PI and GC param
sample_param.df <- merge(sample_GC_param.df, sample_PI_param.df)
# merge iso1 and iso2 pheno data with close_strain_symetrical.df
sample_param_iso1.df <- sample_param.df %>%
select_all(.funs = funs(paste0("iso1_", .)))
sample_param_iso2.df <- sample_param.df %>%
select_all(.funs = funs(paste0("iso2_", .)))
close_strain_symetrical_with_pheno.df <- merge(close_strain_symetrical.df,
sample_param_iso1.df,
by.x = "iso1",
by.y = "iso1_sample_id") %>%
merge(.,
sample_param_iso2.df,
by.x = "iso2",
by.y = "iso2_sample_id")
# Alternative merging
df_all_genetic_pairs_pheno <- close_strain_symetrical.df %>%
left_join(sample_param_iso1.df,
by = c("iso1" = "iso1_sample_id")) %>%
left_join(sample_param_iso2.df,
by = c("iso2" = "iso2_sample_id"))
write_csv(df_all_genetic_pairs_pheno,
"Ideas_Grant_2020_analysis/Genetic_pairs_table/df_all_genetic_pairs_pheno.csv")
# number of unique strains in the genetic pairs
n_distinct(close_strain_symetrical.df$iso1)
## [1] 281
# of these how many are phenotyped
n_distinct(close_strain_symetrical_with_pheno.df$iso1)
## [1] 202
close_strain_symetrical_with_pheno_changes.df <- close_strain_symetrical_with_pheno.df %>%
mutate(delta_time_of_max_rate_OD = iso2_time_of_max_rate_OD - iso1_time_of_max_rate_OD) %>%
mutate(delta_max_rate_OD = iso2_max_rate_OD - iso1_max_rate_OD) %>%
mutate(delta_doubling_time_OD = iso2_doubling_time_OD - iso1_doubling_time_OD) %>%
mutate(delta_AUC_OD = iso2_AUC_OD - iso1_AUC_OD) %>%
mutate(delta_time_of_max_OD = iso2_time_of_max_OD - iso1_time_of_max_OD) %>%
mutate(delta_time_of_min_OD = iso2_time_of_min_OD - iso1_time_of_min_OD) %>%
mutate(delta_max_OD = iso2_max_OD - iso1_max_OD) %>%
mutate(delta_min_OD = iso2_min_OD - iso1_min_OD) %>%
mutate(delta_end_point_OD = iso2_end_point_OD - iso1_end_point_OD) %>%
mutate(delta_time_of_max_rate_death = iso2_time_of_max_rate_death - iso1_time_of_max_rate_death) %>%
mutate(delta_max_rate_death = iso2_max_rate_death - iso1_max_rate_death) %>%
mutate(delta_doubling_time_death = iso2_doubling_time_death - iso1_doubling_time_death) %>%
mutate(delta_AUC_death = iso2_AUC_death - iso1_AUC_death) %>%
mutate(delta_time_of_max_death = iso2_time_of_max_death - iso1_time_of_max_death) %>%
mutate(delta_time_of_min_death = iso2_time_of_min_death - iso1_time_of_min_death) %>%
mutate(delta_max_death = iso2_max_death - iso1_max_death) %>%
mutate(delta_min_death = iso2_min_death - iso1_min_death) %>%
mutate(delta_end_point_death = iso2_end_point_death - iso1_end_point_death) %>%
mutate(log2fc_time_of_max_rate_OD = log2(iso2_time_of_max_rate_OD / iso1_time_of_max_rate_OD)) %>%
mutate(log2fc_max_rate_OD = log2(iso2_max_rate_OD / iso1_max_rate_OD)) %>%
mutate(log2fc_doubling_time_OD = log2(iso2_doubling_time_OD / iso1_doubling_time_OD)) %>%
mutate(log2fc_AUC_OD = log2(iso2_AUC_OD / iso1_AUC_OD)) %>%
mutate(log2fc_time_of_max_OD = log2(iso2_time_of_max_OD / iso1_time_of_max_OD)) %>%
mutate(log2fc_time_of_min_OD = log2(iso2_time_of_min_OD / iso1_time_of_min_OD)) %>%
mutate(log2fc_max_OD = log2(iso2_max_OD / iso1_max_OD)) %>%
mutate(log2fc_min_OD = log2(iso2_min_OD / iso1_min_OD)) %>%
mutate(log2fc_end_point_OD = log2(iso2_end_point_OD / iso1_end_point_OD)) %>%
mutate(log2fc_time_of_max_rate_death = log2(iso2_time_of_max_rate_death / iso1_time_of_max_rate_death)) %>%
mutate(log2fc_max_rate_death = log2(iso2_max_rate_death / iso1_max_rate_death)) %>%
mutate(log2fc_doubling_time_death = log2(iso2_doubling_time_death / iso1_doubling_time_death)) %>%
mutate(log2fc_AUC_death = log2(iso2_AUC_death / iso1_AUC_death)) %>%
mutate(log2fc_time_of_max_death = log2(iso2_time_of_max_death / iso1_time_of_max_death)) %>%
mutate(log2fc_time_of_min_death = log2(iso2_time_of_min_death / iso1_time_of_min_death)) %>%
mutate(log2fc_max_death = log2(iso2_max_death / iso1_max_death)) %>%
mutate(log2fc_min_death = log2(iso2_min_death / iso1_min_death)) %>%
mutate(log2fc_end_point_death = log2(iso2_end_point_death / iso1_end_point_death))
## Warning in mask$eval_all_mutate(dots[[i]]): NaNs produced
## Warning in mask$eval_all_mutate(dots[[i]]): NaNs produced
## Warning in mask$eval_all_mutate(dots[[i]]): NaNs produced
close_strain_symetrical_with_pheno_changes.df <- close_strain_symetrical_with_pheno_changes.df %>%
mutate(switches = ifelse(iso1_mortality == "Survived" & iso2_mortality == "Died", "Survived-Died", NA)) %>%
mutate(switches = ifelse(iso1_mortality == "Survived" & iso2_mortality == "Survived", "Survived-Survived", switches)) %>%
mutate(switches = ifelse(iso1_mortality == "Died" & iso2_mortality == "Died", "Died-Died", switches)) %>%
mutate(switches = ifelse(iso1_mortality == "Died" & iso2_mortality == "Survived", "Died-Survived", switches)) # %>%
#select(iso1, iso2, iso1_mortality, iso2_mortality, switches)
# export the final table
close_strain_symetrical_with_pheno_changes.df %>%
write_csv("Ideas_Grant_2020_analysis/Genetic_pairs_table/genetic_pairs_pheno_changes_mortality_switches.csv")
for (var in grep("delta", colnames(close_strain_symetrical_with_pheno_changes.df), value = T)) {
t <- ggviolin(data = close_strain_symetrical_with_pheno_changes.df,
y = var,
x = "switches",
fill = "switches", add = "jitter"
) +
theme_bw() +
theme(legend.position = "none")+
stat_compare_means(ref.group ="Survived-Survived",
method = "wilcox.test",
label = "p.signif")
print(t)
}
close_strain_symetrical_with_pheno_changes_no_dup.df <- close_strain_symetrical_with_pheno_changes.df %>%
rowwise() %>%
mutate(key = paste(sort(c(iso1, iso2, switches)), collapse = "")) %>%
#select(iso1, iso2, switches, key)
distinct(key, .keep_all = T)
for (var in grep("delta", colnames(close_strain_symetrical_with_pheno_changes_no_dup.df), value = T)) {
t <- ggviolin(data = close_strain_symetrical_with_pheno_changes_no_dup.df,
y = var,
x = "switches",
fill = "switches", add = "jitter"
) +
theme_bw() +
theme(legend.position = "none")+
stat_compare_means(ref.group ="Survived-Survived",
method = "wilcox.test",
label = "p.signif")
print(t)
}
# plot only survived-survived and survived-died
close_strain_symetrical_with_pheno_changes_no_dup.df <- close_strain_symetrical_with_pheno_changes_no_dup.df %>%
filter(switches %in% c("Survived-Survived", "Survived-Died"))
for (var in grep("delta", colnames(close_strain_symetrical_with_pheno_changes_no_dup.df), value = T)) {
t <- ggboxplot(data = close_strain_symetrical_with_pheno_changes_no_dup.df,
y = var,
x = "switches",
fill = "switches", add = "jitter"
) +
theme_bw() +
theme(legend.position = "none")+
stat_compare_means(ref.group ="Survived-Survived",
method = "wilcox.test",
label = "p.signif")
print(t)
}
for (var in grep("log2fc", colnames(close_strain_symetrical_with_pheno_changes.df), value = T)) {
t <- ggboxplot(data = close_strain_symetrical_with_pheno_changes_no_dup.df,
y = var,
x = "switches",
fill = "switches", add = "jitter"
) +
theme_bw() +
theme(legend.position = "none")+
stat_compare_means(ref.group ="Survived-Survived",
method = "wilcox.test",
label = "p.signif")
print(t)
}
## Warning: Removed 238 rows containing non-finite values (stat_boxplot).
## Warning: Removed 238 rows containing non-finite values (stat_compare_means).
## Warning: Removed 134 rows containing missing values (geom_point).
## Warning: Removed 37 rows containing non-finite values (stat_boxplot).
## Warning: Removed 37 rows containing non-finite values (stat_compare_means).
## Warning: Removed 37 rows containing missing values (geom_point).
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## Warning: Removed 3 rows containing non-finite values (stat_compare_means).
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## Warning: Removed 28 rows containing missing values (geom_point).
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## Warning: Removed 54 rows containing non-finite values (stat_compare_means).
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 76 rows containing non-finite values (stat_boxplot).
## Warning: Removed 76 rows containing non-finite values (stat_compare_means).
## Warning: Removed 76 rows containing missing values (geom_point).
for (var in grep("delta", colnames(close_strain_symetrical_with_pheno_changes.df), value = T)) {
t <- ggboxplot(data = close_strain_symetrical_with_pheno_changes_no_dup.df %>% filter(CC %in% c("CC1", "CC22", "CC8")),
y = var,
x = "switches",
fill = "switches", add = "jitter", facet.by = "CC",
) +
theme_bw() +
theme(legend.position = "none")+
stat_compare_means(ref.group ="Survived-Survived",
method = "wilcox.test",
label = "p.signif")
print(t)
}
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
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for (var in grep("log2fc", colnames(close_strain_symetrical_with_pheno_changes.df), value = T)) {
t <- ggboxplot(data = close_strain_symetrical_with_pheno_changes_no_dup.df %>% filter(CC %in% c("CC1", "CC22", "CC8")),
y = var,
x = "switches",
fill = "switches", add = "jitter", facet.by = "CC",
) +
theme_bw() +
theme(legend.position = "none")+
stat_compare_means(ref.group ="Survived-Survived",
method = "wilcox.test",
label = "p.signif")
print(t)
}
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
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## Warning: Removed 184 rows containing non-finite values (stat_boxplot).
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## Warning: Removed 44 rows containing non-finite values (stat_boxplot).
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## Warning: Removed 62 rows containing non-finite values (stat_boxplot).
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## Warning: Removed 62 rows containing missing values (geom_point).
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
for (var in grep("delta", colnames(close_strain_symetrical_with_pheno_changes.df), value = T)) {
t <- ggviolin(data = close_strain_symetrical_with_pheno_changes_no_dup.df %>% filter(dist == 0),
y = var,
x = "switches",
fill = "switches", add = "jitter", label = "iso1"
) +
theme_bw() +
theme(legend.position = "none")+
stat_compare_means(ref.group ="Survived-Survived",
method = "wilcox.test",
label = "p.signif")
print(t)
}
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default
## Warning: Computation failed in `stat_compare_means()`:
## argument "x" is missing, with no default